Movement processing is an emerging research area for analyzing increasing amounts of movement data. This data is typically given as time-stamped sequences of locations, called trajectories, which represent the movement of an entity or an agent in space. Often trajectories are created by sampling GPS locations and attaching a time-stamp, but they can also originate from RFID tags, video, or radar analysis. Huge data sets exist for entities as diverse as ants, fish, birds, deer, traveling humans, sport players, vehicles, and hurricanes.

During recent years automated analysis tools for movement data have been developed, including methods for clustering, performing similarity analysis, segmenting a trajectory, and characterizing patterns such as flocking. Since these computations are mostly spatial, algorithmic solutions have been developed in the areas of computational geometry and GIScience.

Simultaneously, in the area of ecology the study of animal motion has also received increasing interest, in particular, the study of collective motion whereby a group of individuals move in coordination, with or without a specific leader. Although the motivation for the observed movement patterns are various, e.g., foraging, escape from predators, or change of climatic conditions, the mode of movement is mainly determined by social interactions, energy efficiency, distribution of resources, and the natural environment.

This Dagstuhl seminar will address three main topics which go beyond traditional movement analysis and help to provide a way forward at the interface of automated methodologies of trajectory analysis and certain topics in ecology, in particular collective motion, and motion interaction.

Moving entity data processing.

Many questions in interaction and collective motion can only be solved if the movement of an agent is captured and pre-processed suitably. We plan to address situations where a simple GPS reading to get a location at a certain time is insufficient. Relevant research questions include: How to integrate movement paths collected from different sources (e.g. video, RFID, GPS) into one common type, and attach meta-data on quality that is relevant for the analysis? How to model entities and entity motion beyond single-point representations?

Incorporating auxiliary data in movement data analysis.

Merging external factors, e.g. land-cover, weather, environmental heterogeneity, and internal motivation, e.g. heart-rate, emotional state, hunger level, into the analysis of moving agents promises to open up a plethora of novel and exciting possibilities. Relevant research questions include: How to model the heterogeneity of the environment, and how to develop general analysis methods for movement paths in such environments. How to model changes in motion of an entity due to direct or indirect interaction with another entity, and how to compute such changes eff iciently? How to categorize behavioral states and their consequence on individual's motion from the analysis?

Collective motion analysis.

Entities often move in groups that form, split, merge, and end. On a smaller time scale, collective behavior and mutual influence can be seen. Modeling this behavior and coupling it to the different motivations for entities to move is a challenging task. Relevant research questions include: How to define characteristics of collective motionfrom the movement paths of its entities and how to compute these? How to formalize collective motion such that it can be compared across two separate populations of moving entities? How to infer and model an individual's interactions with its close neighbors from its movement path? How to relate collective motion to the environment as referred to in the second topic?

Specific goals.

These topics are relatively unexplored, in particular, when it comes to thoseaspects of mutual interest to ecologists and computer scientists. Collective motion has not been viewed from an algorithmic perspective, and we plan to set the first steps toward establishing real collaboration between researchers from these two fields. We hope and expect that the cross-fertilization will lead to new insights and new directions in either area.

• Better understanding an integration of ecology and computer science research communities (related to movement analysis).
• New insights into the challenges and solution directions relating to the questions listed in this text.
• Setting a research agenda for collective movement analysis.
• Initiating interdisciplinary collaboration.

• Ulf Blanke (ETH Zürich, CH) [dblp]
• Kevin Buchin (TU Eindhoven, NL) [dblp]
• Maike Buchin (Ruhr-Universität Bochum, DE) [dblp]
• Edward A. Codling (University of Essex, GB)
• Matt Duckham (The University of Melbourne, AU) [dblp]
• Roland Geraerts (Utrecht University, NL) [dblp]
• Luca Giuggioli (University of Bristol, GB) [dblp]
• Asja Jelic (CNR - Rome, IT) [dblp]
• Andrea Kölzsch (MPI für Ornithologie - Radolfzell, DE) [dblp]
• Patrick Laube (ZHAW - Wädenswil, CH) [dblp]
• Maarten Löffler (Utrecht University, NL) [dblp]
• Ugo Lopez (Université Paul Sabatier - Toulouse, FR)
• Ran Nathan (The Hebrew University of Jerusalem, IL) [dblp]
• Ko Nishino (Drexel Univ. - Philadelphia, US) [dblp]
• Nicholas Ouellette (Yale University, US) [dblp]
• Andrea Perna (Uppsala University, SE) [dblp]
• Ross Purves (Universität Zürich, CH) [dblp]
• Stef Sijben (Ruhr-Universität Bochum, DE) [dblp]
• Jack Snoeyink (University of North Carolina at Chapel Hill, US) [dblp]
• Bettina Speckmann (TU Eindhoven, NL) [dblp]
• Frank Staals (Utrecht University, NL) [dblp]
• Greg J. Stephens (VU University - Amsterdam, NL) [dblp]
• Guy Theraulaz (Université Paul Sabatier - Toulouse, FR) [dblp]
• Gilles Tredan (LAAS - Toulouse, FR) [dblp]
• Stefan van der Spek (TU Delft, NL) [dblp]
• Marc van Kreveld (Utrecht University, NL) [dblp]
• Yusu Wang (Ohio State University - Columbus, US) [dblp]
• Carola Wenk (Tulane University - New Orleans, US) [dblp]
• Yossi Yovel (Tel Aviv University, IL) [dblp]